Useful Lessons on Datacamp

Assigned course chapters

Note: This list is subject to revision as the semester progresses.

• After Session 1:
  • Try out any datacamp course – just earn 1000 points to complete the assignment
  • If you want to brush up on fundamentals:
    • Introduction to R is good for basics
    • Intermediate R is good for dealing with standard functions
  • If you want to learn something new, take a look at the Other useful courses below
• After Session 2:
  • Correlation and Regression (1 chapter)
    • Chapter 4: Interpretting regression models
  • Introduction to the Tidyverse (1 chapter)
    • Chapter 1: Data wrangling
• After Session 7:
  • Sentiment Analysis in R the Tidy Way (1 chapter)
    • Chapter 1: Tweets across the United States
    • If this type of data interests you, I encourage you to do more of this course!
• After Session 9:
  • Introduction to Machine Learning (1 chapter)
    • Chapter 1: What is machine learning
    • If this type of data interests you, I encourage you to do more of this course!
      • Or consider doing the Supervised Learning in R: Classification or Unsupervised Learning in R courses

Other useful courses on Datacamp

These courses are freely available for your exploration. I have looked through these courses, but have not taken all of them, and thus I cannot say for certain that they are of high quality, but the topics they cover are useful for analytics.

Tutorials on tools

• Working with the RStudio IDE Part 1: The first chapter of this course has a number of videos intended to get you familiar with the IDE. Ignore some of the more technical stuff included though, such as git or shiny – those are outside the scope of our course

Coding techniques

• Writing Functions in R: Functions allow you to greatly expand what you can do in R, by letting you create your own callable functions that you can use over and over.
• Introduction to the Tidyverse: Tidyverse is a collection of a bunch of useful packages supported by RStudio. These packages help make things simpler or more tidy when coding in R.

Data techniques

• Importing Data in R (Part 1):In class we will mostly import using csv files. This course will teach you how to import from many other sources, with a focus on Excel files (which are quite common in business).
• Importing Data in R (Part 2): Chapters 3 and 4 on importing data from the web are useful if you want to scrape webpages.
• Joining Data in R with dplyr: A useful course for those who want to master data manipulation, such as fusing and breaking apart datasets in useful ways.
• Data Visualization with ggplot2 (Part 1): Learn how to make nice plots in R using a very flexible plotting system, ggplot2.
• Data Visualization in R with lattice: Learn how to make nice plots in R using an alternative graphics system, lattice. Has some nice tricks for making nicely formatted graphics.
• Data Visuzlization in R: Learn how to make plots using the built in graphics functionality of R.

Analysis techinques

• Introduction to Time Series Analysis: Time series analysis is particularly useful when working with rich data sets about a particular entity (such as your employer). While we don’t discuss these methods much in class, they are good to learn.
• Introduction to Machine Learning: Related to content discussed in sessions 9 onwards.
• Machine Learning in the Tidyverse: Related to content in sessions 9 onwards.
• Unsupervised Learning in R: Discusses basic computer learning techniques in which the computer learns on its own.
• Supervised Learning in R: Classification: Discusses basic computer learning techniques in which the computer learns based on provided examples (supervision).
• Sentiment Analysis in R: Learn other methods of exploring the sentiment of text. Related to Sessions 7 and 8 on text data.